BankChurners Project

Problem Definition and Questions to be Answered

Objective

**STRUCTURE OF THE DATA**

**EDA**

**Univariate Analysis**

**Bivariate Analysis**

**Summary of EDA**

**Description of Data:**

**Observations:**

**DATA PRE-PROCESSING**

**Encoding**

**Model Prep**

**Missing Value Treatment**

**One Hot Encoding**

**Model Building**

**Logistic Regression**

**Logistic Regression - Oversampled data**

**Logistic Regression - Undersampled data**

**Logistic Regression - Tuned**

**Model Performance**

**Decision Tree**

**Decision Tree - Oversampled data**

**Decision Tree - Undersampled data**

**Decision Tree - Tuned**

**Model Performance**

**Random Forest**

**Random Forest - Oversampled data**

**Random Forest - Undersampled data**

**Random Forest - Tuned**

**Model Performance**

**Bagging**

**Bagging - Oversampled data**

**Bagging - Undersampled data**

**Bagging - Tuned**

**Model Performance**

**AdaBoost**

**AdaBoost - Oversampled data**

**AdaBoost - Undersampled data**

**AdaBoost - Tuned**

**Model Performance**

**Gradient Boosting**

**Gradient Boosting - Oversampled data**

**Gradient Boosting - Undersampled data**

**Gradient Boosting - Tuned**

**Model Performance**

There are 125 False negatives in the gb_classifier under sampled set.

**XGBoost**

**XGBoost - Oversampled data**

**XGBoost - Undersampled data**

**XGBoost - Tuned**

**Model Performance**

**Hyperparameter Tuning Using Random Search and Grid Search**

**AdaBoost Grid Search**

**AdaBoost Random Search**

**Decision Tree Grid Search**

**Decision Tree Random Search**

**XGBoost Grid Search**

**XGBoost Random Search**

**Model Performance**

**Pipelines**

**Conclusion**

**Actionable Insights**

**Grid Search and Random Search**

**Recommedations**

**Advice to Grow Business**

**Created by Stephen Catalfio**